SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2251–2275 of 3304 papers

TitleStatusHype
CASS: Cross Adversarial Source Separation via Autoencoder—0
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces—0
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Multi-view Locality Low-rank Embedding for Dimension Reduction—0
Online Distributed Estimation of Principal Eigenspaces—0
Comparison of Machine Learning Models in Food Authentication Studies—0
EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction—0
Dimensionality Reduction for Tukey Regression—0
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques—0
ForestDSH: A Universal Hash Design for Discrete Probability Distributions—0
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning—0
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data—0
Learning Embeddings into Entropic Wasserstein SpacesCode0
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices—0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewCode0
Guided Visual Exploration of Relations in Data SetsCode0
Locally Differentially Private Naive Bayes Classification—0
Uncertainty-Aware Principal Component AnalysisCode0
Network Representation Learning: Consolidation and Renewed BearingCode0
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery—0
Locally Linear Unsupervised Feature Selection—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Representation-Constrained Autoencoders and an Application to Wireless Positioning—0
Online Learning for Supervised Dimension Reduction—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified